where \(F_{in}\)  and \(F_{out}\)  represent the input and output features respectively, \(w\)  denotes the kernel weights.
Due to its powerful spatial adaption ability, deformable convolution has been widely used in various tasks such as video object detection [1]}, action recognition [2]}, semantic segmentation [3]} and video super-resolution [4]}.
For VFI, [5]} and [6]} first introduced the DConv layer in kernel-based methods, where additional offsets were calculated to adaptively sample useful information from the input frames. Gui et al. and Chen et al. [7]}, [8]} used the DConv layer to directly generate the intermediate frame through blending deep features. Shi et al. [9]} proposed a more generalized DConv layer, which extracted the information from the multiple frames to solve the performance-limiting issues of conventional methods. Different from the above methods that directly use the DConv layer to model the complete motions, we employ a flow guidance DConv layer to compensate for the missing details of the motion-based methods.
